Cognitive, behavioral, and psychological manifestations of COVID-19 in post-acute rehabilitation setting: preliminary data of an observational study
Bibliographic record
Abstract
Psychological, emotional, and behavioral domains could be altered in COVID-19 patients and measurement of variables within these domains seems to be mandatory. Neuropsychological assessment could detect possible cognitive impairment caused by COVID-19 and the choice of appropriate tools is an important question. Aim of this exploratory study was to verify the effectiveness of an assessment model for patients with COVID-19. Twelve patients were enrolled and tested with Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Anxiety and Depression Short Scale (AD-R), and the Neuropsychiatry Inventory (NPI), at the time of their entrance (T0) and discharge (T1) from a rehabilitative unit. Moreover, a follow-up evaluation after 3 months (T2) has been conducted on eight patients. Results showed that at baseline (T0), 58.3% of the patients reported a score below cut-off at MMSE and 50% at MoCA. Although a significant amelioration was found only in NPI scores, a qualitative improvement has been detected at all tests, except for MoCA scores, in the T0-T1 trend analysis. A one-way repeated measures analysis of variance showed a significant variation in AD-R depression score, considering the three-assessment time (T0, T1, and T2). The evaluation and tracking over time of the impact of COVID-19 on cognitive, psychological, and behavioral domains has relevant implications for rehabilitation and long-term assistance needs planning. The choice of assessment tools should consider patients vulnerability and match the best compromise among briefness, sensitivity, and specificity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".